Chapter 4 MAG catalogue

load("data/data_25082025.Rdata")

4.1 Genome phylogeny

# Generate the phylum color heatmap
phylum_heatmap <- read_tsv("https://raw.githubusercontent.com/earthhologenome/EHI_taxonomy_colour/main/ehi_phylum_colors.tsv") %>%
    right_join(genome_metadata, by=join_by(phylum == phylum)) %>%
    arrange(match(genome, genome_tree$tip.label)) %>%
    select(genome,phylum) %>%
    mutate(phylum = factor(phylum, levels = unique(phylum))) %>%
    column_to_rownames(var = "genome")

# Generate new species table
newspecies_table <- genome_metadata %>% 
  mutate(newspecies=ifelse(species=="s__","Y","N")) %>% 
  select(genome,newspecies) %>% 
  column_to_rownames(var = "genome")

# Generate  basal tree
circular_tree <- force.ultrametric(genome_tree, method="extend") %>% # extend to ultrametric for the sake of visualisation
  ggtree(., layout="fan", open.angle=10, size=0.5)
***************************************************************
*                          Note:                              *
*    force.ultrametric does not include a formal method to    *
*    ultrametricize a tree & should only be used to coerce    *
*   a phylogeny that fails is.ultrametric due to rounding --  *
*    not as a substitute for formal rate-smoothing methods.   *
***************************************************************
# Add phylum ring
circular_tree <- gheatmap(circular_tree, phylum_heatmap, offset=0.03, width=0.1, colnames=FALSE) +
  scale_fill_manual(values=phylum_colors) +
  #geom_tiplab2(size=1, hjust=-0.1) +
  theme(legend.position = "none", plot.margin = margin(0, 0, 0, 0), panel.margin = margin(0, 0, 0, 0))

# Flush color scale to enable a new color scheme in the next ring
circular_tree <- circular_tree + new_scale_fill()

# Add completeness ring
circular_tree <- circular_tree +
  new_scale_fill() +
  scale_fill_gradient(low = "#d1f4ba", high = "#f4baba") +
  geom_fruit(
    data=genome_metadata,
    geom=geom_bar,
    mapping = aes(x=completeness, y=genome, fill=contamination),
    offset = 0.3,
    orientation="y",
    stat="identity")

circular_tree <- circular_tree + new_scale_fill()
circular_tree <- gheatmap(circular_tree, newspecies_table, offset=0.5, width=0.05, colnames=FALSE) +
  scale_fill_manual(values=c("#f4f4f4","#666666"))


# Add text
circular_tree <-  circular_tree +
  annotate('text', x=2.9, y=0, label='              Phylum', family='ArialMT', size=3.5) +
  annotate('text', x=3.6, y=0, label='                         Genome quality', family='ArialMT', size=3.5) +
  annotate('text', x=3.2, y=0, label='                      New species', family='ArialMT', size=3.5)

#Plot circular tree
circular_tree %>% open_tree(30) %>% rotate_tree(90)

4.2 Genome quality

genome_metadata %>%
    summarise(completeness_mean=mean(completeness) %>% round(2) %>% as.character(),
              completeness_sd=sd(completeness) %>% round(2) %>% as.character(),
              contamination_mean=mean(contamination) %>% round(2),
              contamination_sd=sd(contamination) %>% round(2)) %>%
    unite("Completeness",completeness_mean, completeness_sd, sep = " ± ", remove = TRUE) %>%
    unite("Contamination",contamination_mean, contamination_sd, sep = " ± ", remove = TRUE) %>%
    tt()
Completeness Contamination
92.99 ± 7.17 2.15 ± 2.19
#Generate quality biplot
genome_biplot <- genome_metadata %>%
  select(c(genome,domain,phylum,completeness,contamination,length)) %>%
  arrange(match(genome, rev(genome_tree$tip.label))) %>% #sort MAGs according to phylogenetic tree
  ggplot(aes(x=completeness,y=contamination,size=length,color=phylum)) +
              geom_point(alpha=0.7) +
                    ylim(c(10,0)) +
                    scale_color_manual(values=phylum_colors) +
                    labs(y= "Contamination", x = "Completeness") +
                    theme_classic() +
                    theme(legend.position = "none")

#Generate contamination boxplot
genome_contamination <- genome_metadata %>%
            ggplot(aes(y=contamination)) +
                    ylim(c(10,0)) +
                    geom_boxplot(colour = "#999999", fill="#cccccc") +
                    theme_void() +
                    theme(legend.position = "none",
                        axis.title.x = element_blank(),
                        axis.title.y = element_blank(),
                        axis.text.y=element_blank(),
                        axis.ticks.y=element_blank(),
                        axis.text.x=element_blank(),
                        axis.ticks.x=element_blank(),
                        plot.margin = unit(c(0, 0, 0.40, 0),"inches")) #add bottom-margin (top, right, bottom, left)

#Generate completeness boxplot
genome_completeness <- genome_metadata %>%
        ggplot(aes(x=completeness)) +
                xlim(c(50,100)) +
                geom_boxplot(colour = "#999999", fill="#cccccc") +
                theme_void() +
                theme(legend.position = "none",
                    axis.title.x = element_blank(),
                    axis.title.y = element_blank(),
                    axis.text.y=element_blank(),
                    axis.ticks.y=element_blank(),
                    axis.text.x=element_blank(),
                    axis.ticks.x=element_blank(),
                    plot.margin = unit(c(0, 0, 0, 0.50),"inches")) #add left-margin (top, right, bottom, left)

#Render composite figure
grid.arrange(grobs = list(genome_completeness,genome_biplot,genome_contamination),
        layout_matrix = rbind(c(1,1,1,1,1,1,1,1,1,1,1,4),
                              c(2,2,2,2,2,2,2,2,2,2,2,3),
                              c(2,2,2,2,2,2,2,2,2,2,2,3),
                              c(2,2,2,2,2,2,2,2,2,2,2,3),
                              c(2,2,2,2,2,2,2,2,2,2,2,3),
                              c(2,2,2,2,2,2,2,2,2,2,2,3),
                              c(2,2,2,2,2,2,2,2,2,2,2,3),
                              c(2,2,2,2,2,2,2,2,2,2,2,3),
                              c(2,2,2,2,2,2,2,2,2,2,2,3),
                              c(2,2,2,2,2,2,2,2,2,2,2,3),
                              c(2,2,2,2,2,2,2,2,2,2,2,3),
                              c(2,2,2,2,2,2,2,2,2,2,2,3)))

4.3 Functional overview

4.3.1 Predicted genes

# Predicted genes
pred_genes <- genome_annotations %>%
  nrow()

cat(pred_genes)
930700

4.3.2 Number of annotated genes and percentages (doesn’t work)

# Some annotation
genome_annota <- genome_annotations %>%
  filter(if_any(c(ko_id, pfam_hits, cazy_hits), ~ !is.na(.))) %>%
  nrow()

cat(genome_annota)
700149
# Some annotation percentage
genome_annota*100/pred_genes
[1] 75.22822

4.3.3 Number of KEGG annotatated genes and percentages

# KEGG annotation
kegg_annota <- genome_annotations %>%
  filter(!is.na(ko_id)) %>%
  nrow()
cat(kegg_annota)
412617
# KEGG annotation percentage
kegg_annota*100/genome_annota
[1] 58.93274
# Aggregate basal GIFT into elements
function_table <- genome_gifts %>%
    to.elements(., GIFT_db)

# Generate  basal tree
function_tree <- force.ultrametric(genome_tree, method="extend") %>%
                ggtree(., size = 0.3) 
***************************************************************
*                          Note:                              *
*    force.ultrametric does not include a formal method to    *
*    ultrametricize a tree & should only be used to coerce    *
*   a phylogeny that fails is.ultrametric due to rounding --  *
*    not as a substitute for formal rate-smoothing methods.   *
***************************************************************
#Add phylum colors next to the tree tips
function_tree <- gheatmap(function_tree, phylum_heatmap, offset=0, width=0.1, colnames=FALSE) +
            scale_fill_manual(values=phylum_colors) +
            labs(fill="Phylum")

#Reset fill scale to use a different colour profile in the heatmap
function_tree <- function_tree + new_scale_fill()

#Add functions heatmap
function_tree <- gheatmap(function_tree, function_table, offset=0.5, width=3.5, colnames=FALSE) +
            vexpand(.08) +
            coord_cartesian(clip = "off") +
            scale_fill_gradient(low = "#f4f4f4", high = "steelblue", na.value="white") +
            labs(fill="GIFT")

#Reset fill scale to use a different colour profile in the heatmap
function_tree <- function_tree + new_scale_fill()

# Add completeness barplots
function_tree <- function_tree +
            geom_fruit(data=genome_metadata,
            geom=geom_bar,
            grid.params=list(axis="x", text.size=2, nbreak = 1),
            axis.params=list(vline=TRUE),
            mapping = aes(x=length, y=genome, fill=completeness),
                 offset = 3.8,
                 orientation="y",
                 stat="identity") +
            scale_fill_gradient(low = "#cf8888", high = "#a2cc87") +
            labs(fill="Genome\ncompleteness")

function_tree

4.4 Functional ordination

gift_pcoa <- genome_gifts %>%
    to.elements(., GIFT_db) %>%
    as.data.frame() %>%
    vegdist(method="euclidean") %>%
    pcoa()

gift_pcoa_rel_eigen <- gift_pcoa$values$Relative_eig[1:10]


# Get genome positions
gift_pcoa_vectors <- gift_pcoa$vectors %>% #extract vectors
  as.data.frame() %>% 
  select(Axis.1,Axis.2) # keep the first 2 axes

gift_pcoa_eigenvalues <- gift_pcoa$values$Eigenvalues[c(1,2)]

gift_pcoa_gifts <- cov(genome_gifts, scale(gift_pcoa_vectors)) %*% diag((gift_pcoa_eigenvalues/(nrow(genome_gifts)-1))^(-0.5)) %>%
  as.data.frame() %>% 
  rename(Axis.1=1,Axis.2=2) %>% 
  rownames_to_column(var="label") %>% 
  #get function summary vectors
  mutate(func=substr(label,1,3)) %>% 
  group_by(func) %>% 
  summarise(Axis.1=mean(Axis.1),
            Axis.2=mean(Axis.2)) %>% 
  rename(label=func) %>% 
  filter(!label %in% c("S01","S02","S03"))
set.seed(101)
scale <- 20 # scale for vector loadings
gift_pcoa_vectors %>% 
  rownames_to_column(var="genome") %>% 
  left_join(genome_metadata, by="genome") %>%
  ggplot() +
      #genome positions
      scale_color_manual(values=phylum_colors)+
      geom_point(aes(x=Axis.1,y=Axis.2, color=phylum, size=length), 
                 alpha=0.9, shape=16) +
      #scale_color_manual(values=phylum_colors) +
      scale_size_continuous(range = c(0.1,5)) +
      #loading positions
      geom_segment(data=gift_pcoa_gifts, 
                   aes(x=0, y=0, xend=Axis.1 * scale, yend=Axis.2 * scale),
                    arrow = arrow(length = unit(0.3, "cm"), 
                    type = "open", 
                    angle = 25),
                    linewidth = 0.5, 
                    color = "black") +
     #Primary and secondary scale adjustments
     scale_x_continuous(name = paste0("PCoA1 (",round(gift_pcoa_rel_eigen[1]*100, digits = 2), " %)"),
                      sec.axis = sec_axis(~ . / scale, name = "Loadings on PCoA1")
            ) +
     scale_y_continuous(name = paste0("PCoA2 (",round(gift_pcoa_rel_eigen[2]*100, digits = 2), " %)"),
                      sec.axis = sec_axis(~ . / scale, name = "Loadings on PCoA2")
            ) +
    geom_label_repel(data = gift_pcoa_gifts,
                     aes(label = label, x = Axis.1 * scale, y = Axis.2 * scale),
                     segment.color = 'transparent') +
    xlim(-4.5,4.5) + 
    ylim(-3,2.5) +
    theme_minimal() + 
    theme(legend.position = "none")